Network traffic  management

ABSTRACT

In an aspect, the present application is directed to managing traffic in a network. A capacity request is received, wherein the capacity request comprises a request for capacity on the network. The capacity request is evaluated to determine whether the capacity requested by the capacity request in view of a measure of currently in-use network capacity on the network would exceed a threshold level. If the threshold level would not be exceeded, the capacity request is fulfilled so that a corresponding network request can be granted for transmission over the network; and if the threshold level would be exceeded, retrieving and analyzing real-time capacity data to determine an available network capacity of the network to be compared to the capacity requested in the capacity request.

TECHNICAL FIELD

The description is directed generally to networks such astelecommunication networks and/or machine to machine (M2M) networks,particularly to traffic management for such networks to optimize thenetwork capacity and, in particular, to a computer-implemented method, acomputer system, and a computer program product to manage traffic in anetwork.

BACKGROUND

In a network, collections of nodes, links and any intermediate nodes areconnected via links of the network so as to enable telecommunication(also referred to as communication) between them. This communicationbetween nodes in a network is commonly referred to as network traffic ortraffic. The links connecting the nodes together can be wireless orwired, and the nodes use circuit switching, message switching, and/orpacket switching to pass data and/or signals through the correct linksand nodes to reach the correct destination node. Each node in a networkusually has a unique address so messages can be routed to the correctrecipient node(s).

Links between the nodes in a network may define a (communication)channel between the nodes. A channel may be also referred to as a bandand can be described by its bandwidth, or capacity for traffic. Thenodes may be any kind of (technical/electronic) devices and/or(computer) systems.

Examples of networks are telecommunication networks such as computernetworks, the Internet, the telephone network, and the global Telexnetwork.

Other examples of networks are machine to machine (M2M) networks. M2Mrefers to technologies that allow wireless, wired, and hybrid (i.e. bothwired and wireless) systems to communicate with each other. M2Mcommunication can be a one-to-one connection (such as a remote networkof machines relaying information back to a central hub for processingwhich would be rerouted into a computer system such as a PC) or a systemof networks that transmits data to personal appliances. M2M networkshave in the past been used for automation and instrumentation but arenowadays more commonly used in telematics applications. Indeed, M2M hasnumerous applications. For example, wireless M2M networks that are allinterconnected can serve to improve production and efficiency in variousareas such as machinery that works on building cars and/or on lettingthe developers of products know when those products need to be taken infor maintenance and/or for other reasons, such as reporting performanceindications. Another exemplary application is to use wireless M2Mtechnology to monitor systems such as utility meters. A furtherexemplary application is to use wireless M2M networks to updatebillboards. And, as noted above, telematics and in-vehicle entertainmentis also an area of focus for M2M developers.

Traffic management systems are used to control and/or manage traffic innetworks, particularly when available network capacity is limited, sothat performance of the network can be optimized. Traffic managementsystem can be thus used, for instance, to minimize the impact of peakusage in terms of failed connections between nodes in a network and/oracross different networks and/or the quality of service of a network.Quality of service refers to several related aspects of (telephonyand/or computer) networks that prioritize the transport of traffic withspecific requirements, such as voice traffic. Network capacity relatesto the maximum capacity of one or more channels of the network totransmit data from one node in the network to another node in thenetwork. Traffic management systems can be used to optimize the networkcapacity of a network for performance tuning particularity with regardto bandwidth. Network capacity can be based on the channel capacity ofeach of the channels in the network and the channel capacity may definethe tightest upper bound on the rate of data that can be reliablytransmitted over a channel.

Capacity management as performed by a traffic management system is aprocess used to manage network capacity to meet current and futurerequirements in an effective manner. Since the usage of nodes and/orchannels in the network change even over short periods of time and, overlonger periods, node functionality evolves as the amount of processingpower, memory, bandwidth need, etc. changes. If there are spikes in, forexample, bandwidth need at a particular time, there must be processes inplace to analyze what is happening at that time and makes changes to thetraffic distribution to maximize available network capacity.

In traffic management systems that are used to control traffic onnetworks where available network capacity is limited, control decisionsare made in real-time based on data about network traffic that isgathered in real-time. Such traffic management systems require a modelof the network capacity also referred to as a capacity model, a model ofthe network congestion scenarios (network congestion can occur when achannel an/or a node of the network is carrying so much data that itsquality of service deteriorates), the ability to control network accessas performed by a traffic controller, and the ability to measure currentnetwork utilization in real-time. A capacity model can define capacityof a network and can specify a maximum number of radio frequencies inthe network, a maximum number of active circuits in the network, amaximum number of concurrent sessions in the network, and/or a maximumallowed bandwidth of the network.

As a consequence of needing to be able to gather network information inreal-time, currently available traffic management systems are complex.This is due to the need to retrieve real time data from a large numberof channels and/or devices of the network and to use such real-time datato calculate the currently (i.e. at a given point in time) availablenetwork capacity. Such calculations require constantly probing thenetwork and real time data elaboration. In other words, current trafficmanagement systems are required to retrieve real time data from a largenumber of network channels and/or nodes to determine a current networkcapacity. Consequently, currently available traffic management systemscan be inefficient (regarding processing time and/or memory, computingresources), and expensive.

Hence, there is a need to provide systems and methods for addressing theabove problems to provide traffic management for networks with enhancedperformance (regarding processing time/memory, computing resources,bandwidth use) which only require access to real time data according inspecific conditions only (this can be also referred to as a“just-in-time” analysis) to reduce the amount of real-time data andreal-time analysis required so as to be less complex and also cheapercompared to currently available traffic management systems and methods.

SUMMARY

According to one general aspect a computer-implemented method to managetraffic in a network is provided. The method comprises: receiving acapacity request from a node on the network, wherein the capacityrequest comprises a request for capacity on the network; evaluatingwhether the capacity requested with the capacity request in view of ameasure currently in-use network capacity on the network would exceed athreshold level; if the threshold level would not be exceeded, thecapacity request is fulfilled so that a corresponding network requestcan be granted for transmission over the network; and if the thresholdlevel would be exceeded, retrieving and analyzing real-time capacitydata to determine an available network capacity of the network to becompared to the capacity requested in the capacity request.

The capacity request requesting network capacity to process acorresponding network request on the network can be evaluated usingstandard analytical tools to retrieve statistical data on the requestedcapacity. The statistical data can comprise a bandwidth need to processthe network request, estimated parameters required for processing thenetwork request, and/or an average time required for processing thenetwork request.

The threshold level determines an optimal network capacity for thenetwork, and is computed by applying s service level estimation model(such as the, newsvendor model, which is described further herein) tothe statistical data corresponding to the capacity request. The measureof currently in-use network capacity of the network can be estimatedusing a capacity model for the network, which can be a standard modelfor modeling network capacity.

The method manages and/or controls traffic (such as communication) in anetwork (e.g. a telecommunication network, a M2M network) to optimizethe network capacity, in particular, where available capacity of thenetwork is limited. This allows for minimizing the impact of peak usagein terms of failed connections between nodes in the network and/orbetween a node form a different network connecting to a node in saidnetwork and/or for quality of service of the network.

The method optimizes the network's capacity including channel capacityand/or node capacity. The method enables optimization of networkcapacity without requiring constantly probing the network and real timedata elaboration. In other words, constant retrieval of real time datafrom a large number of network channels and/or nodes to determine acurrent network capacity is avoided. In this way, network traffic isreduced and performance (regarding processing time/memory, computingresources, bandwidth use) of the network is improved. Further,processing capacity requests is performance tuned since a just-in-timeanalysis is performed using the service level estimation model todetermine the threshold level for the capacity request. This reduces theamount of real-time data and real-time analysis required so as to beless complex and also cheaper compared to currently available trafficmanagement systems and methods.

Preferably, the threshold level corresponds to a confidence level thatnetwork capacity will not be exceeded.

Preferably, evaluating whether the capacity requested with the capacityrequest in view of the measure of currently in-use network capacity onthe network would exceed a threshold level is performed by applying aservice level estimation model to the capacity requested with thecapacity request and the measure of currently in-use network capacity onthe network.

Preferably, the service level estimation model is used to determine thethreshold value by applying the capacity requested in the capacityrequest and the measure of currently in-use network capacity of thenetwork to the service level estimation model. Using the service levelestimation model, one or more or all of the following data (or datavalues) retrieved and/or derived from the capacity request and/or thecurrent in-use capacity of the network can be evaluated using theservice level estimation model: an average utilization time for acapacity unit (e.g. a node of the network)=T and/or a standard deviationof the utilization time of a capacity unit=σ. An input parameter whichis also referred to as a confidence factor or confidential level “k” isutilized. “k” relates to the service level (called a), i.e. theprobability that an actual capacity utilization time will not exceedT+k*σ. Under the assumption of normal distribution, a service level of:95% K→1.64, 99% K→2.32, 99.9% K→3.09.

Preferably, the service level estimation model is an implementation of anewsvendor model.

Preferably, the network is a telecommunication network.

Preferably, the network is a machine to machine network.

According to another general aspect, a computer system to manage trafficin a network (also referred to as a traffic management system herein) isprovided. The system comprises: a request interface (or a networkinterface) operable to receive a capacity request, wherein the capacityrequest comprises a request for capacity on the network; and ananalytics engine operable to evaluate whether the capacity requestedwith the capacity request in view of a measure of currently in-usenetwork capacity on the network would exceed a threshold level. If thethreshold level would not be exceeded, the capacity request is fulfilledso that a corresponding network request can be granted for transmissionover the network. If the threshold level would be exceeded, a real-timedata regarding the network capacity is retrieved and analyzed todetermine an available network capacity of the network. The availablenetwork capacity is compared to the capacity requested in the capacityrequest.

Preferably, the system is operable to implement any of the definedmethods.

In another general aspect there is provided a computer-program productcomprising computer readable instructions, which when loaded and run ina computer system and/or computer network system, cause the computersystem and/or the computer network system to perform a method asdescribed.

The subject matter described in this specification can be implemented asa method or as a system or using computer program products, tangiblyembodied in information carriers, such as a CD-ROM, a DVD-ROM, asemiconductor memory, signal and/or data stream, and a hard disk. Suchcomputer program products may cause a data processing apparatus toconduct one or more operations described in this specification.

In addition, the subject matter described in this specification can alsobe implemented as a system including a processor and a memory coupled tothe processor. The memory may encode one or more programs that cause theprocessor to perform one or more of the method acts described in thisspecification. Further the subject matter described in thisspecification can be implemented using various MRI machines.

Details of one or more implementations are set forth in the accompanyingexemplary drawings and exemplary description below. Other features willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary network implemented with a traffic managementsystem and method.

FIG. 2 shows an exemplary service level estimation model which can beimplemented with the traffic management system and method of figure.

FIG. 3 shows an exemplary computer system for implementing methods andsystems as shown in FIGS. 1 and 2.

DETAILED DESCRIPTION

In the following, a detailed description of examples will be given withreference to the drawings. It should be understood that variousmodifications to the examples may be made. In particular, elements ofone example may be combined and used in other examples to form newexamples.

The present application generally describes computing and networktechnologies for the management of the network capacity of a network. Anetwork comprises collections of nodes, links (also referred to aschannels) and any intermediate nodes which can connect via links of thenetwork to establish link connections so as to enable telecommunication(also referred to as communication) between the nodes.

Preferably, the network is a telecommunication network. Preferably, thenetwork is a M2M network, and further preferably a narrowband M2Mnetwork. Narrowband describes a channel in which the bandwidth of amessage communicated between nodes in the network does not significantlyexceed the channel's coherence bandwidth.

New systems and methods are described to manage and/or to controltraffic in networks, in particular, where an available network capacityis limited. The described traffic management systems and methods can beused to minimize the impact of peak usage in terms of failed linkconnections between nodes in a network and/or across different networksand/or the quality of service of a network. Therefore, the describedtraffic management systems and methods can be used to optimize thenetwork capacity of a network for performance tuning particularity withregard to bandwidth.

Network capacity relates to the maximum capacity of one or more channelsof the network to transmit data from one node in the network to anothernode in the network. Traffic management systems can be thus used tooptimize the network capacity of a network for performance tuningparticularity with regard to bandwidth. Network capacity can be based onthe channel capacity of each of the channels in the network. Channelcapacity may define the tightest upper bound on the rate of data thatcan be reliably transmitted over a channel.

The present application aims to minimize failed link connections in anetwork and maximizing quality of service of a network. In other words,the present application aims at an optimized network capacity of anetwork. The traffic management systems and methods as described hereinachieve this aim by reducing retrieval of real time data and processingof real time data in order to determine a network capacity for acapacity request. Network capacity, preferably optimized networkcapacity, is computed (or determined) by evaluating whether a requestfor a capacity of a network (also referred to herein as a capacityrequest) does not exceed a threshold level. A capacity request can besent from a node within or outside a network before sending acorresponding data and/or processing request itself in order todetermine whether the network has capacity to process the request. Thethreshold level is set based on a confidential level that the networkcapacity will not be exceed. For example, assuming

-   -   a maximum capacity of “M” in a specific network node,    -   an average utilization time for a capacity unit (e.g. a resource        in a node or a node of the network)=T,    -   standard deviation of the utilization time of a capacity unit=σ,    -   α being the service level,    -   a confidence level “k”,    -   T-Count=number of capacity requests taken place in the interval:        T+k*σ (which relates to the probability that an actual capacity        utilization time will not exceed this value T-count with a        service level α; T-Count is therefore referred to as the        threshold level and shall not exceed M).

Assuming that n is the number of capacity requests for a given node ofthe network received in the preceding time equal to the threshold levelT-Count. If n is equal to M, potentially the capacity has been fullyutilized and before fulfilling any additional request, a trafficcontroller (preferably in interaction with a real-time data retriever)will have to retrieve real time data of the (current) utilization ofsaid node in order to avoid oversubscription.

The optimal service level estimation is based on a heuristic model forapproximating an optimal inventory in a supply chain also known as the“newsvendor model”.

The traffic management systems and methods operate on estimated and/orpredictive data regarding a capacity request (also referred to asstatistical data of a capacity request) and the service level estimationmodel. The statistical data of the capacity request can be derived fromperiodic data analysis of completed network activity. The statisticaldata of the capacity request to be estimated include an averageutilization time for a capacity unit (e.g. a resource of a node, a nodein the network)=T, and/or a standard deviation of the utilization timeof a capacity unit=σ. Said statistical data can be related to globalaverages such as on call network nodes and/or can be specialized such asper network node and/or per request (type).

The service level estimation model is applied to the statistical data ofthe capacity request in view of the threshold level to manage the vastmajority of network requests and/or to thereby minimize (or at leastreduce) the amount of real-time data retrieval and real-time dataanalysis needed to operate the network with optimized network capacity.

FIG. 1 shows a traffic management system 100 implementing acorresponding traffic management method for management and/or control ofa network 300 such as a telecommunication network or a M2M network. Thenetwork 300 comprises a plurality of nodes 310 (also referred to asnetwork nodes 310). The traffic management system 100 is connected tothe network 300. The traffic management system is also operable toretrieve one or more capacity requests 200 requesting capacity of thenetwork for a corresponding network request. The network request relatesto a request for data and/or data processing within the network 300. Acapacity request 200 can be retrieved from a node 310 of the network 300and/or from a node outside the network 300. A capacity request 200 canbe specified for example via a web service, a JMS message, or similarrequest, and comprises data regarding a network request including dataand/or information needed to retrieve the information on the one or morenodes involved in the capacity request. A capacity request 200 thusinvolves traffic in the network 300.

The traffic management system 100 processes a capacity request 200 todetermine whether the capacity request 200 is fulfilled so that thecorresponding network request can be processed in the network 300. Forthis purpose, the traffic management system 100 analyzes the capacityrequest 200 in view of an available capacity of the network 300,preferably at a given point in time and/or during a given period of timeto retrieve statistical data with regard to the capacity request 200. Anavailable capacity of the network 300 can be estimated and/or determinedthrough evaluation of a capacity model 130 and/or past and/or currenttraffic in the network 300. The statistical data of the capacity request200 is then used in a service level estimation model 110 to determinewhether a threshold level is met. The service level estimation model 110is described in detail below with reference to FIG. 2.

To implement the above computations, the traffic management system 100can comprises a request interface 120, a capacity model 130, ananalytics engine 140, a real time data retriever 150, a trafficcontroller 160, and/or a node interface 170.

The traffic management system is also operable to retrieve and/or tointercept one or more capacity requests 200 requesting capacity of thenetwork for a corresponding network 300 request at the request interface120. The interface 120 is operable to accept incoming a capacity request200. In case of available capacity in the network 300, a positiveacknowledgement message is sent to the requestor of the capacity request200. In case of unavailable capacity with regard to the capacity request200, a negative response message is sent to the requestor of thecapacity request 200.

The traffic management system 100 is operable to connect to nodes 310 ofthe network 300 through the node interface 170. The node interface 170is operable to receive data and/or information regarding the real timestatus of the capacity utilization of nodes 310 and/or toterminate/abort in real time the status of existing connections betweennodes 310 in the network 300.

The capacity model 130 is a standard model specifying capacity of thenetwork 300. The capacity model 130 can be based on predictive dataanalytics of the network 300. Using the capacity model 130, theestimation of currently utilized resources in at least one node 310, canbe performed by the following computation: The number of utilizedcapacity units in time T0 is equal to the number of requests received inthe interval (T0−T-Count, T0).

The real time data retriever 150 is a standard network componentoperable to retrieve real time data regarding the network 300 includingchannels and nodes in the network 300. The real time data retriever 150is operable to utilize the node interface 170 to retrieve real time dataand/or information of the status with regard to a capacity request 200in a given node 310 in the network 300.

The traffic controller 160 is a standard network component operable tocontrol network activities in the network 300, preferably regarding pastand/or current traffic in the network 300. The traffic controller 160may also be operable to coordinate allocation and/or deallocation ofnodes 310 in the network 300 in response to a network request. For acapacity request 200 received at the traffic management system 100, thetraffic controller 160 is operable to perform the following operations:Estimate by accessing the capacity model 130, if capacity according tothe capacity request 200 is (probably) available. If based on theanalysis of the capacity model 130 capacity for the capacity request 200is available, the traffic controller 160 sends a positiveacknowledgement message via the capacity interface 120 to the requestorof the capacity request 200. The corresponding request can be thenprocessed in the network 300.

If based on the analysis of the capacity model 130 capacity for thecapacity request 200 is not available, the traffic controller 160 sendsa negative response message via the capacity interface 120 to therequestor of the capacity request 200. In this case, the trafficcontroller 160 further interacts with the real time data retriever 150to retrieve real time data in order to check a real time status of thenetwork 300 with regard to the capacity. Preferably, if based on theanalysis of the capacity model 130 capacity for the capacity request 200is not available and if the request 200 has normal priority, the trafficcontroller 160 sends a negative response message via the capacityinterface 120 to the requestor of the capacity request 200. if based onthe analysis of the capacity model 130 capacity for the capacity request200 is not available and if the request 200 has a high priority, thetraffic controller 160 holds in abeyance or aborts an existing capacityrequest 200 and sends a positive acknowledgement message via thecapacity interface 120 to the requestor of the capacity request 200.

The analytics engine 140 is operable to process capacity requests 200retrieved at the traffic management system 100. The analytics engine 140may use for this processing the capability model 130, the service levelestimation model 130, the real time data retriever 150, and/or thetraffic controller 160. The analytics engine 140 can be operable todetermine the statistical data for the capacity request 200 including[please give a specification of said statistics] and/or to determine,using the service level estimation model 110 applied to the statisticaldata of the capacity request 200 whether a threshold level is met ornot.

Regarding the statistical data of the capacity request 200, theanalytics engine 140 can be operable to compute an average time for thereceived capacity request 200 to be fulfilled. The average time for acapacity request 200 can be periodically re-evaluated by utilizingclustering to segment retrieved capacity requests 110 and estimatingsegmented utilization averages, with a segmentation, for example pernode type, per incoming request type, per time of the day.

Using the analytics engine 140 and/or a combination of one or more ofthe components 120, 130, 140, 150, 160, 170, the traffic managementsystem 100 is operable to evaluate whether a capacity request 200 isfulfilled by the network 300 or not. Whether the capacity request 200 isfulfilled or not can be determined by evaluating whether the capacityrequested with the capacity request in view of a current in-use networkcapacity of the network 300 exceeds a threshold level regarding thenetwork capacity or not. The current (at a given point in time) in-usenetwork capacity of the network 300 preferably “results from anevaluation of the capacity model 130. The threshold level is determinedusing the service level estimation model 110 applied to the statisticaldata of the capacity request and/or the current in-use network capacityof the network 300.

The components 120, 130, 140, 150, 160, 170 of the traffic managementsystem 100 can be also implemented in a single component forming thetraffic management 100 itself and/or further other components having thefunctionality described herein.

A capacity request 200 is received and/or intercepted at the trafficmanagement system 100. The traffic management system 100 processesand/or evaluates the capacity request 200 to predict, to estimate and/orto approximate statistical data regarding a corresponding networkrequest for which capacity is requested with the capacity request 200including how much bandwidth is needed, parameters required, and/or anaverage time for processing the corresponding network request in thenetwork 300. The average time for processing are periodicallyre-evaluated based on completed network activity in the network 300 overa predetermined time period (e.g. on a daily basis).

The traffic management system 100 evaluates the statistical data of thecapacity request 200 in view of a current in-use network capacity of thenetwork 300 as determined using the capacity model 130 by applying thecorresponding data to the service level estimation model 110 in order toevaluate and/or determine whether the capacity request 200 in view ofthe current in-use capacity of the network 300 would exceed a thresholdlevel or not. In other words, the statistical data regarding thecapacity request 200 and the current in-use capacity of the network 300regarding the capacity model 130 are evaluated to retrieve a result. Forexample, data being evaluated may comprise an average capacityutilization duration in the network 300 over a predetermined period oftime and its standard deviation. The result can be a value which can becompared to the threshold level. The threshold level is preferably basedon a confidence level that network capacity of the network 300 will notbe exceeded.

If according to a result of the evaluation, using the statistical dataof the capacity request 300 and the service level estimation model 110,the network capacity is (substantially) optimal, preferably if thethreshold level would not be exceeded, the capacity request 200 isfulfilled and the corresponding network request can be processed in thenetwork 300. Else, the capacity request 200 is delayed and the trafficmanagement system 100 retrieves real time data (preferably by triggeringthe real time data retriever 150 accordingly) on the available capacityin the specific one or more nodes 310 of the network 300 according tothe capacity request 200. In other words, in this case, the trafficmanagement system 100 probes real time data to determine availablecapacity in the specific nodes 310 according to the capacity request200. The capacity request 200 is then fulfilled, aborted, or held inabeyance based on the retrieved effective capacity data (e.g. the realtime data retrieved).

FIG. 2 shows a service level estimation model 110 which can beimplemented with the traffic management system 100 as shown in FIG. 1 todetermine a (available) network capacity of the network 300 with regardto statistical data of a capacity request 200 in view of a currentin-use capacity of the network 300.

The service level estimation model 110 is an implementation of the newsvendor model, which, as explained above, is a mathematical model used inoperations management and applied economics used to determine optimalinventory levels. The newsvendor model defines the complex mathematicalproblem that can be described by analogy with the situation faced by anewspaper vendor how must decide how many copies of the day's paper tostock in the face of uncertain demand knowing that unsold copies with beworthless at the end of the day. A unique aspect of the implementationof the news vendor model in this context is that network capacity is nottraditionally viewed as akin to physical inventory; application of themodel as suggested herein however permits available network capacity tobe estimated in a way that does not require a constant stream ofreal-time data.

Applying the newsvendor model in terms of the service level estimationmodel 110, the model 110 is used to determine optimal threshold levelsfor an optimal network capacity by applying the model 110 to thestatistical data obtained by evaluation of a capacity request 200 and acapacity model 130 of a network 300 to which the request 200 isdirected. A threshold level is based on a confidence level that thenetwork capacity of a network 300 will not be exceeded.

In other words, the service level estimation model 110 is used toestimate an available network capacity of a network 300 at a given pointin time to determine whether a capacity request 200 to the network 300is fulfilled. A threshold level for the available network capacity isestimated. The threshold level is set based on a confidence level thatnetwork capacity will not be exceeded.

To be applied to evaluate optimal network capacity of a network 300, inthe service level estimation model 110 the used variables are defined asfollows:

-   -   a maximum capacity of “M” in a specific network node,    -   an average utilization time for a capacity unit (e.g. a resource        in a node or a node of the network)=T,    -   standard deviation of the utilization time of a capacity unit=σ,    -   α being the service level,    -   a confidence level “k”,    -   T-Count=number of capacity requests taken place in the interval:        T+k*σ (which relates to the probability that an actual capacity        utilization time will not exceed this value T-count with a        service level α; T-Count is therefore referred to as the        threshold level and shall not exceed M).

Assuming that n is the number of capacity requests for a given node ofthe network received in the preceding time equal to the threshold levelT-Count. If n is equal to M, potentially the capacity has been fullyutilized and before fulfilling any additional request, a trafficcontroller (preferably in interaction with a real-time data retriever)will have to retrieve real time data of the (current) utilization ofsaid node in order to avoid oversubscription.

The service level α can be evaluated using the newsvendor model as shownin FIG. 2:

u=is the unfulfilled capacity requesto=is the cost of unused capacity, this is preferably evaluated using thecost real time capacity check

FIG. 3 shows an exemplary system for implementing the inventionincluding a general purpose computing device in the form of a computingdevice 920. As examples, computing device 920 may take the form of adesktop computer, a server computer, a network router, a network switch,or other telecommunications device. In some implementations, device 920may include one or more devices 920 each including one or morecomponents of a device 920. The device 920 may perform one or moreprocesses described herein.

The computing device 920 includes a processor 922, a system memory 924,and a system bus 926. The system bus couples various system componentsincluding the system memory 924 to the processor 922. The processor 922may perform arithmetic, logic and/or control operations by accessing thesystem memory 924. The system memory 924 may store information and/orinstructions for use in combination with the processor 922. Processor922 may include a processor (e.g., a central processing unit, a graphicsprocessing unit, an accelerated processing unit), a microprocessor, or asimilar processing component that interprets and/or executesinstructions. The system memory 924 may include volatile andnon-volatile memory, such as a random access memory (RAM) 928 and a readonly memory (ROM) 930. A basic input/output system (BIOS) containing thebasic routines that helps to transfer information between elementswithin the computing device 920, such as during start-up, may be storedin the ROM 930. The system bus 926 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures.

Computing device 920 may further include a hard disk drive 932 forreading from and writing to a hard disk (not shown), and an externaldisk drive 934 for reading from or writing to a removable disk 936. Theremovable disk 936 may be a magnetic disk for a magnetic disk drive oran optical disk such as a CD ROM for an optical disk drive. The harddisk drive 932 and the external disk drive 934 are connected to thesystem bus 926 by a hard disk drive interface 938 and an external diskdrive interface 940, respectively. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer readableinstructions, data structures, program modules and other data for thecomputing device 920. The data structures may include relevant data forthe implementation of the method for providing a femtocell-basedinfrastructure for mobile electronic payment, as described above. Therelevant data may be organized in a database, for example a relationaldatabase management system or an object-oriented database managementsystem.

Although the exemplary computing device 920 described herein employs ahard disk (not shown) and an external disk 936, it should be appreciatedby those skilled in the art that other types of computer readable mediawhich can store data that is accessible by a computer, such as magneticcassettes, solid state (i.e. flash) memory, digital video disks, randomaccess memories, read only memories, and the like, may also be used inthe exemplary operating environment.

Computing device 920 may perform one or more of the processes describedherein, and/or it may perform these processes in response to processor922 executing software instructions included in a computer-readablemedium, such as RAM 928, ROM 930, hard disk drive 932, removable disk936, or other non-transitory memory device. Such a memory device mayinclude memory space within a single physical storage device or memoryspace spread across multiple physical storage devices. Morespecifically, a number of program modules may be stored on the hard diskdrive 932, removable disk 936, ROM 930 or RAM 928, including anoperating system (not shown), one or more application programs 944,other program modules (not shown), and program data 946. The applicationprograms may include at least a part of the functionality as depicted inFIGS. 1 and 2. In some implementations, one or more aspects of thesystem 100 depicted FIG. 1 may be performed by computing device 920. Insome implementations, one or more aspects of the system 100 depicted inFIG. 1 may be performed by another computing device or a group ofcomputing devices separate from or including computing device 920.

A user may enter commands and information, as discussed below, into thecomputing device 920 through input devices such as keyboard 948 andmouse 950. Other input devices (not shown) may include a microphone (orother sensors), joystick, game pad, scanner, or the like. These andother input devices may be connected to the processor 922 through aserial port interface 952 that is coupled to the system bus 926, or maybe collected by other interfaces, such as a parallel port interface 954,game port or a universal serial bus (USB). Further, information may beprinted using printer 956. The printer 956 and other parallelinput/output devices may be connected to the processor 922 throughparallel port interface 954. A monitor 958 or other type of displaydevice is also connected to the system bus 926 via an interface, such asa video input/output 960. In addition to the monitor, computing device920 may include other peripheral output devices (not shown), such asspeakers or other audible output.

The computing device 920 may communicate with other electronic devicessuch as a computer, telephone (wired or wireless), personal digitalassistant, television, or the like. To communicate, the computer device920 may operate in an M2M networked environment using connections to oneor more electronic devices. FIG. 3 depicts the computer environmentnetworked with remote computer 962. The remote computer 962 may beanother computing environment such as a server, a router, a network PC,a peer device or other common network node, and may include many or allof the elements described above relative to the computing device 920.The logical connections depicted in FIG. 3 include a local area network(LAN) 964 and a wide area network (WAN) 966. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets and the Internet and may particularly be encrypted.

When used in a LAN networking environment, the computing device 920 maybe connected to the LAN 964 through a network I/O 968. When used in aWAN networking environment, the computing device 920 may include a modem970 or other means for establishing communications over the WAN 966. Themodem 970, which may be internal or external to computing device 920, isconnected to the system bus 926 via the serial port interface 952. In anetworked environment, program modules depicted relative to thecomputing device 920, or portions thereof, may be stored in a remotememory storage device resident on or accessible to remote computer 962.Furthermore other data relevant to the method for managing traffic in anetwork (described above) may be resident on or accessible via theremote computer 962. It will be appreciated that the network connectionsshown are exemplary and other means of establishing a communicationslink between the electronic devices may be used.

The above-described computing device 920 is only one example of the typeof computing device that may be used to implement the method formanaging traffic in a network.

1. Computer-implemented method to manage traffic in a network, themethod comprising: receiving, at a network interface, a capacityrequest, wherein the capacity request comprises a request for capacityon the network; evaluating, by at least one processor, whether thecapacity requested by the capacity request in view of a measure ofcurrently in-use network capacity on the network would exceed athreshold level; if the threshold level would not be exceeded, thecapacity request is fulfilled so that a corresponding network requestcan be granted for transmission over the network; and if the thresholdlevel would be exceeded, retrieving and analyzing real-time capacitydata to determine an available network capacity of the network to becompared to the capacity requested in the capacity request.
 2. Themethod according to claim 1, wherein the threshold level corresponds toa confidence level that network capacity of the network will not beexceeded.
 3. The method according to claim 1, wherein evaluating whetherthe capacity requested with the capacity request in view of the measureof currently in-use network capacity on the network would exceed athreshold level is performed by applying a service level estimationmodel to the capacity requested with the capacity request and themeasure of currently in-use network capacity on the network.
 4. Themethod according to claim 3, wherein the service level estimation modelis used to determine the threshold value by applying the capacityrequested in the capacity request and the measure of currently in-usenetwork capacity of the network to the service level estimation model.5. The method according to claim 3, wherein the service level estimationmodel is an implementation of a newsvendor model.
 6. The methodaccording to claim 1, wherein the network is a telecommunicationnetwork.
 7. The method according to claim 1, wherein the network is amachine to machine network.
 8. A non-transitory computer readable mediumstoring a computer program product comprising computer readableinstructions, which when loaded and run in a computer system, causes oneor more processors in the computer system to perform operationscomprising: receiving a capacity request, wherein the capacity requestcomprises a request for capacity on the network; evaluating whether thecapacity requested by the capacity request in view of a measure ofcurrently in-use network capacity on the network would exceed athreshold level; if the threshold level would not be exceeded, thecapacity request is fulfilled so that a corresponding network requestcan be granted for transmission over the network; and if the thresholdlevel would be exceeded, retrieving and analyzing real-time capacitydata to determine an available network capacity of the network to becompared to the capacity requested in the capacity request.
 9. Computersystem to manage traffic in a network, the system comprising: a requestinterface operable to receive a capacity request, wherein the capacityrequest requests for capacity on the network; and an analytics engine,executed on one or more processors of the computer system, operable toevaluate whether the capacity requested with the capacity request inview of a measure of currently in-use network capacity on the networkwould exceed a threshold level; wherein if the threshold level would notbe exceeded, the capacity request is fulfilled so that a correspondingnetwork request can be granted for transmission over the network; and ifthe threshold level would be exceeded, a real time data retriever isoperable to retrieve and analyzing real-time capacity data to determinean available network capacity of the network to be compared to thecapacity requested in the capacity request.
 10. The system according toclaim 9, wherein the threshold level corresponds to a confidence levelthat network capacity of the network will not be exceeded.
 11. Thesystem according to claim 9, wherein the analytical engine is operableto evaluate whether the capacity requested with the capacity request inview of the measure of currently in-use network capacity on the networkwould exceed a threshold level is performed by applying a service levelestimation model to the capacity requested with the capacity request andthe measure of currently in-use network capacity on the network.
 12. Thesystem according to claim 11, wherein the service level estimation modelis used to determine the threshold value by applying the capacityrequested in the capacity request and the measure of currently in-usenetwork capacity of the network to the service level estimation model.13. The system according to claim 11, wherein the service levelestimation model is an implementation of a newsvendor model.
 14. Thesystem according to claim 9, wherein the network is a telecommunicationnetwork.
 15. The system according to claim 9, wherein the network is amachine to machine network.